Gooey confusion

From Week 7 of the lesson... Didn't really get what is Support Vector Machines (SVM) initially so went to google up a bit and watch some videos.  Summary: It is a supervised learning models used to analysis regression or classification problems Divide the classes of the data by a clear gap (aka "hyperplane") that is as wide as possible Can be used to classify multi-dimensional data thus the classifier is known as "hyperplane" Recommended Videos: Support Vector Machine (SVM) - Fun and Easy Machine Learning (7 mins): by Augmented Startups https://www.youtube.com/watch?v=Y6RRHw9uN9o  Support Vector Machines - The Math of Intelligence (Week 1) (30 mins); by Siraj Raval https://www.youtube.com/watch?v=g8D5YL6cOSE Learning: Support Vector Machines (49 mins): by MIT OpenCourseWare https://www.youtube.com/watch?v=_PwhiWxHK8o Also learnt about Gaussian Kernel ... It is used when there is a non-linear decision b...

Sorry for the lack of updates...Have been rushing to complete the Machine Learning course as fast as possible... Good news!!! We managed to survive through the whole course!!!! Hahaha Summary of Week 6 lesson: Some definitions first before delving into the details... Bias:  The hypothesis is known to be biased if it tends to overestimate or underestimate a parameter Variance:  The hypothesis is known to have high variance if the data are far away from its average value Given the complexity of some data, a linear regression may not be able to formulate an accurate prediction. As the linear regression only factors in 1 specific feature of the data, the 'best-fit' line does not fit the data well enough. Such under-fitting problem leads to high training costs and also causes the hypothesis to be biased. Ways to reduce the bias: Add more features / Increase the degree of the polynomial Decrease λ  Use larger neural network As mentioned a...